app.py

from flask import Flask, render_template, request,redirect
import cv2
import numpy as np

app = Flask(__name__)

@app.route("/",methods=['GET','POST'])
def detect_mask():
   if request.method=='POST': 
      neural_net = cv2.dnn.readNet('yolov3_training_last.weights', 'yolov3_testing.cfg')

      classes = []
      with open("classes.txt", "r") as f:
         classes = f.read().splitlines()

      cap=cv2.VideoCapture(0)

      if not cap.isOpened():
         cap=cv2.VideoCapture(1)
      if not cap.isOpened():
         raise IOError("Cannot open webcam")
      font = cv2.FONT_HERSHEY_PLAIN

      while True:
         _, img = cap.read()
         height, width, _ = img.shape

         treat_img= cv2.dnn.blobFromImage(img, 1/255, (416, 416), (0,0,0), swapRB=True, crop=False)
         neural_net.setInput(treat_img)
         output_layers_names = neural_net.getUnconnectedOutLayersNames()
         layerOutputs = neural_net.forward(output_layers_names)

         bounding_boxes = []
         probabilities = []
         class_labels = []

         for output in layerOutputs:
            for detection in output:
                  prob_values=detection[5:]
                  class_label=np.argmax(prob_values)
                  class_probabilitiy=prob_values[class_label]
                  if class_probabilitiy > 0.2:
                     center_x = int(detection[0]*width)
                     center_y = int(detection[1]*height)
                     w = int(detection[2]*width)
                     h = int(detection[3]*height)

                     x = int(center_x - w/2)
                     y = int(center_y - h/2)

                     bounding_boxes.append([x, y, w, h])
                     probabilities.append((float(class_probabilitiy)))
                     class_labels.append(class_label)

         indexes = cv2.dnn.NMSBoxes(bounding_boxes, probabilities, 0.2, 0.4)

         if len(indexes)>0:
            for i in indexes.flatten():
                  x, y, w, h = bounding_boxes[i]
                  label = str(classes[class_labels[i]])
                  class_probabilitiy =str((round(probabilities[i],2))*100)
                  if class_labels[i]==0:
                     cv2.rectangle(img, (x,y), (x+w, y+h), (0,255,0), 2)
                     cv2.putText(img, label, (x, y+20), font, 1, (0,255,0), 1)
                  else:
                     cv2.rectangle(img, (x,y), (x+w, y+h), (0,0,255), 2)
                     cv2.putText(img, label, (x, y+20), font, 1, (0,0,255), 1)


         cv2.imshow('Webcam For Face Mask Detection', img)
         key = cv2.waitKey(1)
         if key==27:
            break
      
      cap.release()
      cv2.destroyAllWindows()
   
   return render_template("index.html")

if __name__ == "__main__":
     app.run(debug=True,port=8000)